Joint Parameter Estimation and Signal Detection for Integrated Sensing and Backscatter Communication
Bibliographic record
Abstract
In this paper, we investigate the integrated sensing and backscatter communication (ISABC) system in mobility scenarios. Specifically, the backscatter devices (BDs) are attached to the moving targets, thus enhancing the signal strength of reflected echoes and concurrently passively transmitting supplementary information, such as identification details, to the ISABC terminal through backscatter communication. The ISABC terminal aims to detect signals from the BDs while concurrently estimating target parameters, such as delays and Doppler shifts, from the backscattered signals. However, it is quite challenging to concurrently achieve parameter estimation and signal detection from the received superposition of two disparate signals emanating from the structural and antenna components of the target equipped with BD. The challenge is exacerbated coupling between the symbols of BD and the estimated parameters, alongside the intercarrier interference (ICI) induced by the Doppler shift. To address these issues, we propose a novel joint parameter estimation and signal detection scheme by alternatively performing the following two processes: 1) Utilizing a modified maximum likelihood (ML) estimation algorithm to perform off-grid ICI-aware sensing with superimposed signals. 2) Employing the generalized likelihood ratio test (GLRT) detector for demodulating the symbols of the BD. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm and validate that the estimation performance can be improved in the high SNR regime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".